sa_fri1: Synthetic regression and classification datasets for...

In rminer: Data Mining Classification and Regression Methods

Synthetic regression and classification datasets for measuring input importance of supervised learning models

Description

5 Synthetic regression (sa_fri1, sa_ssin, sa_psin, sa_int2, sa_tree) and 4 classification (sa_ssin_2, sa_ssin_n2p, sa_int2_3c, sa_int2_8p) datasets for measuring input importance of supervised learning models

Usage

data(sa_fri1)

Format

A data frame with 1000 observations on the following variables.

xn

input (numeric or factor, depends on the dataset)

y

output target (numeric or factor, depends on the dataset)

Details

Check reference or source for full details

Source

See references

References

• To cite the Importance function, sensitivity analysis methods or synthetic datasets, please use:
  P. Cortez and M.J. Embrechts.
  Using Sensitivity Analysis and Visualization Techniques to Open Black Box Data Mining Models.
  In Information Sciences, Elsevier, 225:1-17, March 2013.
  \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ins.2012.10.039")}
  

Examples

data(sa_ssin)
print(summary(sa_ssin))
## Not run: plot(sa_ssin$x1,sa_ssin$y)

rminer documentation built on Oct. 29, 2024, 9:06 a.m.